Overview

Dataset statistics

Number of variables21
Number of observations681
Missing cells1698
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory573.5 KiB
Average record size in memory862.4 B

Variable types

Categorical2
Text5
Numeric12
DateTime2

Alerts

备注 has constant value ""Constant
手续费更新时间 has constant value ""Constant
价格更新时间 has constant value ""Constant
手续费标准-开仓-万分之 has 363 (53.3%) missing valuesMissing
手续费标准-平昨-万分之 has 363 (53.3%) missing valuesMissing
手续费标准-平今-万分之 has 363 (53.3%) missing valuesMissing
备注 has 609 (89.4%) missing valuesMissing
合约名称 has unique valuesUnique
合约代码 has unique valuesUnique
手续费标准-平今-万分之 has 72 (10.6%) zerosZeros

Reproduction

Analysis started2024-03-21 02:19:19.992099
Analysis finished2024-03-21 02:19:45.260397
Duration25.27 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

交易所名称
Categorical

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
上海期货交易所
211 
大连商品交易所
202 
郑州商品交易所
168 
上海国际能源交易中心
53 
中国金融期货交易所
28 

Length

Max length10
Median length7
Mean length7.3157122
Min length7

Characters and Unicode

Total characters4982
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row上海期货交易所
2nd row上海期货交易所
3rd row上海期货交易所
4th row上海期货交易所
5th row上海期货交易所

Common Values

ValueCountFrequency (%)
上海期货交易所 211
31.0%
大连商品交易所 202
29.7%
郑州商品交易所 168
24.7%
上海国际能源交易中心 53
 
7.8%
中国金融期货交易所 28
 
4.1%
广州期货交易所 19
 
2.8%

Length

2024-03-21T10:19:45.402524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T10:19:45.596683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
上海期货交易所 211
31.0%
大连商品交易所 202
29.7%
郑州商品交易所 168
24.7%
上海国际能源交易中心 53
 
7.8%
中国金融期货交易所 28
 
4.1%
广州期货交易所 19
 
2.8%

Most occurring characters

ValueCountFrequency (%)
681
13.7%
681
13.7%
628
12.6%
370
 
7.4%
370
 
7.4%
264
 
5.3%
264
 
5.3%
258
 
5.2%
258
 
5.2%
202
 
4.1%
Other values (12) 1006
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
681
13.7%
681
13.7%
628
12.6%
370
 
7.4%
370
 
7.4%
264
 
5.3%
264
 
5.3%
258
 
5.2%
258
 
5.2%
202
 
4.1%
Other values (12) 1006
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
681
13.7%
681
13.7%
628
12.6%
370
 
7.4%
370
 
7.4%
264
 
5.3%
264
 
5.3%
258
 
5.2%
258
 
5.2%
202
 
4.1%
Other values (12) 1006
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
681
13.7%
681
13.7%
628
12.6%
370
 
7.4%
370
 
7.4%
264
 
5.3%
264
 
5.3%
258
 
5.2%
258
 
5.2%
202
 
4.1%
Other values (12) 1006
20.2%

合约名称
Text

UNIQUE 

Distinct681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.7 KiB
2024-03-21T10:19:46.767346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.4963289
Min length5

Characters and Unicode

Total characters4424
Distinct characters128
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique681 ?
Unique (%)100.0%

Sample

1st row沪银2401
2nd row沪银2402
3rd row沪银2403
4th row沪银2404
5th row沪银2405
ValueCountFrequency (%)
烧碱 8
 
1.1%
对二甲苯 8
 
1.1%
408 2
 
0.3%
409 2
 
0.3%
410 2
 
0.3%
411 2
 
0.3%
412 2
 
0.3%
406 2
 
0.3%
405 2
 
0.3%
407 2
 
0.3%
Other values (665) 665
95.4%
2024-03-21T10:19:48.152647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 719
16.3%
2 635
 
14.4%
0 615
 
13.9%
1 280
 
6.3%
87
 
2.0%
80
 
1.8%
5 79
 
1.8%
3 78
 
1.8%
9 65
 
1.5%
6 64
 
1.4%
Other values (118) 1722
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 719
16.3%
2 635
 
14.4%
0 615
 
13.9%
1 280
 
6.3%
87
 
2.0%
80
 
1.8%
5 79
 
1.8%
3 78
 
1.8%
9 65
 
1.5%
6 64
 
1.4%
Other values (118) 1722
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 719
16.3%
2 635
 
14.4%
0 615
 
13.9%
1 280
 
6.3%
87
 
2.0%
80
 
1.8%
5 79
 
1.8%
3 78
 
1.8%
9 65
 
1.5%
6 64
 
1.4%
Other values (118) 1722
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 719
16.3%
2 635
 
14.4%
0 615
 
13.9%
1 280
 
6.3%
87
 
2.0%
80
 
1.8%
5 79
 
1.8%
3 78
 
1.8%
9 65
 
1.5%
6 64
 
1.4%
Other values (118) 1722
38.9%

合约代码
Text

UNIQUE 

Distinct681
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2024-03-21T10:19:49.344326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6020558
Min length5

Characters and Unicode

Total characters3815
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique681 ?
Unique (%)100.0%

Sample

1st rowag2401
2nd rowag2402
3rd rowag2403
4th rowag2404
5th rowag2405
ValueCountFrequency (%)
ag2401 1
 
0.1%
ag2409 1
 
0.1%
al2409 1
 
0.1%
al2408 1
 
0.1%
ag2403 1
 
0.1%
ag2404 1
 
0.1%
ag2405 1
 
0.1%
ag2406 1
 
0.1%
ag2407 1
 
0.1%
ag2408 1
 
0.1%
Other values (671) 671
98.5%
2024-03-21T10:19:50.654595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 719
18.8%
2 609
16.0%
0 578
15.2%
1 271
 
7.1%
b 92
 
2.4%
r 80
 
2.1%
s 80
 
2.1%
c 79
 
2.1%
3 71
 
1.9%
p 71
 
1.9%
Other values (40) 1165
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 719
18.8%
2 609
16.0%
0 578
15.2%
1 271
 
7.1%
b 92
 
2.4%
r 80
 
2.1%
s 80
 
2.1%
c 79
 
2.1%
3 71
 
1.9%
p 71
 
1.9%
Other values (40) 1165
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 719
18.8%
2 609
16.0%
0 578
15.2%
1 271
 
7.1%
b 92
 
2.4%
r 80
 
2.1%
s 80
 
2.1%
c 79
 
2.1%
3 71
 
1.9%
p 71
 
1.9%
Other values (40) 1165
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 719
18.8%
2 609
16.0%
0 578
15.2%
1 271
 
7.1%
b 92
 
2.4%
r 80
 
2.1%
s 80
 
2.1%
c 79
 
2.1%
3 71
 
1.9%
p 71
 
1.9%
Other values (40) 1165
30.5%

现价
Real number (ℝ)

Distinct632
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15606.867
Minimum101.3139
Maximum212720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:50.861212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101.3139
5-th percentile554.2
Q12916
median5683
Q310975
95-th percentile100500
Maximum212720
Range212618.69
Interquartile range (IQR)8059

Descriptive statistics

Standard deviation34894.891
Coefficient of variation (CV)2.2358678
Kurtosis17.589754
Mean15606.867
Median Absolute Deviation (MAD)2830
Skewness4.0915106
Sum10628276
Variance1.2176535 × 109
MonotonicityNot monotonic
2024-03-21T10:19:51.054347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2431 3
 
0.4%
3941 2
 
0.3%
12755 2
 
0.3%
6422 2
 
0.3%
6430 2
 
0.3%
1884.5 2
 
0.3%
7382 2
 
0.3%
3703 2
 
0.3%
3646 2
 
0.3%
3812 2
 
0.3%
Other values (622) 660
96.9%
ValueCountFrequency (%)
101.3139 1
0.1%
101.322 1
0.1%
101.328 1
0.1%
101.42 1
0.1%
101.5 1
0.1%
101.7 1
0.1%
102.37 1
0.1%
102.45 1
0.1%
102.545 1
0.1%
102.615 1
0.1%
ValueCountFrequency (%)
212720 1
0.1%
212550 1
0.1%
212020 1
0.1%
211970 1
0.1%
211910 1
0.1%
211760 1
0.1%
211720 1
0.1%
211590 1
0.1%
211570 1
0.1%
211420 1
0.1%

涨停板
Real number (ℝ)

Distinct635
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17022.239
Minimum101.8199
Maximum233990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:51.236653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101.8199
5-th percentile604
Q13194
median6023
Q311630
95-th percentile115550
Maximum233990
Range233888.18
Interquartile range (IQR)8436

Descriptive statistics

Standard deviation38541.267
Coefficient of variation (CV)2.2641714
Kurtosis17.47312
Mean17022.239
Median Absolute Deviation (MAD)3028
Skewness4.0910186
Sum11592145
Variance1.4854293 × 109
MonotonicityNot monotonic
2024-03-21T10:19:51.421525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16795 3
 
0.4%
7448 2
 
0.3%
14155 2
 
0.3%
15545 2
 
0.3%
6020 2
 
0.3%
7908 2
 
0.3%
3228 2
 
0.3%
5956 2
 
0.3%
3214 2
 
0.3%
4242 2
 
0.3%
Other values (625) 660
96.9%
ValueCountFrequency (%)
101.8199 1
0.1%
101.828 1
0.1%
101.834 1
0.1%
103.595 1
0.1%
103.675 1
0.1%
103.775 1
0.1%
104.665 1
0.1%
104.78 1
0.1%
104.905 1
0.1%
104.96 1
0.1%
ValueCountFrequency (%)
233990 1
0.1%
233800 1
0.1%
233220 1
0.1%
233160 1
0.1%
233100 1
0.1%
232930 1
0.1%
232890 1
0.1%
232740 1
0.1%
232720 1
0.1%
232560 1
0.1%

跌停板
Real number (ℝ)

Distinct632
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14190.134
Minimum97.88
Maximum191440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:51.602550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum97.88
5-th percentile504.3
Q12692
median5318
Q310315
95-th percentile85450
Maximum191440
Range191342.12
Interquartile range (IQR)7623

Descriptive statistics

Standard deviation31267.455
Coefficient of variation (CV)2.2034644
Kurtosis17.720484
Mean14190.134
Median Absolute Deviation (MAD)2685
Skewness4.0923583
Sum9663481.3
Variance9.7765372 × 108
MonotonicityNot monotonic
2024-03-21T10:19:51.806616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7755 3
 
0.4%
5576 2
 
0.3%
114620 2
 
0.3%
5778 2
 
0.3%
5786 2
 
0.3%
5568 2
 
0.3%
3731 2
 
0.3%
14885 2
 
0.3%
14895 2
 
0.3%
14905 2
 
0.3%
Other values (622) 660
96.9%
ValueCountFrequency (%)
97.88 1
0.1%
97.95 1
0.1%
98.15 1
0.1%
100.565 1
0.1%
100.68 1
0.1%
100.795 1
0.1%
100.808 1
0.1%
100.816 1
0.1%
100.822 1
0.1%
101.145 1
0.1%
ValueCountFrequency (%)
191440 1
0.1%
191290 1
0.1%
190810 1
0.1%
190770 1
0.1%
190710 1
0.1%
190580 1
0.1%
190540 1
0.1%
190430 1
0.1%
190410 1
0.1%
190270 1
0.1%

保证金-买开
Real number (ℝ)

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.500147
Minimum0.5
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:51.983061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile7
Q18
median9
Q312
95-th percentile20
Maximum22
Range21.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0729506
Coefficient of variation (CV)0.38789463
Kurtosis0.76834006
Mean10.500147
Median Absolute Deviation (MAD)2
Skewness1.0432624
Sum7150.6
Variance16.588926
MonotonicityNot monotonic
2024-03-21T10:19:52.130493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 173
25.4%
7 106
15.6%
12 99
14.5%
20 66
 
9.7%
11 60
 
8.8%
9 47
 
6.9%
10 42
 
6.2%
15 34
 
5.0%
6 16
 
2.3%
13 14
 
2.1%
Other values (6) 24
 
3.5%
ValueCountFrequency (%)
0.5 3
 
0.4%
1.2 3
 
0.4%
2 3
 
0.4%
3.5 3
 
0.4%
6 16
 
2.3%
7 106
15.6%
8 173
25.4%
9 47
 
6.9%
10 42
 
6.2%
11 60
 
8.8%
ValueCountFrequency (%)
22 2
 
0.3%
20 66
 
9.7%
16 10
 
1.5%
15 34
 
5.0%
13 14
 
2.1%
12 99
14.5%
11 60
 
8.8%
10 42
 
6.2%
9 47
 
6.9%
8 173
25.4%

保证金-卖开
Real number (ℝ)

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.500147
Minimum0.5
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:52.271997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile7
Q18
median9
Q312
95-th percentile20
Maximum22
Range21.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0729506
Coefficient of variation (CV)0.38789463
Kurtosis0.76834006
Mean10.500147
Median Absolute Deviation (MAD)2
Skewness1.0432624
Sum7150.6
Variance16.588926
MonotonicityNot monotonic
2024-03-21T10:19:52.678111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 173
25.4%
7 106
15.6%
12 99
14.5%
20 66
 
9.7%
11 60
 
8.8%
9 47
 
6.9%
10 42
 
6.2%
15 34
 
5.0%
6 16
 
2.3%
13 14
 
2.1%
Other values (6) 24
 
3.5%
ValueCountFrequency (%)
0.5 3
 
0.4%
1.2 3
 
0.4%
2 3
 
0.4%
3.5 3
 
0.4%
6 16
 
2.3%
7 106
15.6%
8 173
25.4%
9 47
 
6.9%
10 42
 
6.2%
11 60
 
8.8%
ValueCountFrequency (%)
22 2
 
0.3%
20 66
 
9.7%
16 10
 
1.5%
15 34
 
5.0%
13 14
 
2.1%
12 99
14.5%
11 60
 
8.8%
10 42
 
6.2%
9 47
 
6.9%
8 173
25.4%

保证金-每手
Real number (ℝ)

Distinct646
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13218.823
Minimum1236.5
Maximum141312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:52.847505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1236.5
5-th percentile2060.8
Q13342.4
median5720
Q312071
95-th percentile58817
Maximum141312
Range140075.5
Interquartile range (IQR)8728.6

Descriptive statistics

Standard deviation21146.143
Coefficient of variation (CV)1.5996994
Kurtosis16.446883
Mean13218.823
Median Absolute Deviation (MAD)2838.45
Skewness3.7708047
Sum9002018.2
Variance4.4715935 × 108
MonotonicityNot monotonic
2024-03-21T10:19:53.045808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6336 3
 
0.4%
4826.5 2
 
0.3%
3258.2 2
 
0.3%
8430 2
 
0.3%
8940 2
 
0.3%
2887.15 2
 
0.3%
6340 2
 
0.3%
9000 2
 
0.3%
49570 2
 
0.3%
3322.4 2
 
0.3%
Other values (636) 660
96.9%
ValueCountFrequency (%)
1236.5 1
0.1%
1274 1
0.1%
1286.5 1
0.1%
1298 1
0.1%
1299.5 1
0.1%
1312 1
0.1%
1315 1
0.1%
1319.5 1
0.1%
1342.5 1
0.1%
1695.6 1
0.1%
ValueCountFrequency (%)
141312 1
0.1%
140755.2 1
0.1%
140318.4 1
0.1%
138230.4 1
0.1%
130483.2 1
0.1%
130161.6 1
0.1%
129950.4 1
0.1%
128544 1
0.1%
124408.8 1
0.1%
124315.2 1
0.1%

手续费标准-开仓-万分之
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)6.0%
Missing363
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean0.00011284591
Minimum1.1 × 10-5
Maximum0.000641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:53.230628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 10-5
5-th percentile1.1 × 10-5
Q14.1 × 10-5
median0.000101
Q30.000101
95-th percentile0.000304
Maximum0.000641
Range0.00063
Interquartile range (IQR)6 × 10-5

Descriptive statistics

Standard deviation0.00011115007
Coefficient of variation (CV)0.98497209
Kurtosis7.933966
Mean0.00011284591
Median Absolute Deviation (MAD)5 × 10-5
Skewness2.5519206
Sum0.035885
Variance1.2354339 × 10-8
MonotonicityNot monotonic
2024-03-21T10:19:53.405663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.000101 123
 
18.1%
1.1 × 10-534
 
5.0%
5.1 × 10-530
 
4.4%
4.1 × 10-523
 
3.4%
0.000201 16
 
2.3%
2.3 × 10-516
 
2.3%
0.000301 13
 
1.9%
0.000141 12
 
1.8%
2.1 × 10-511
 
1.6%
0.000151 10
 
1.5%
Other values (9) 30
 
4.4%
(Missing) 363
53.3%
ValueCountFrequency (%)
1.1 × 10-534
 
5.0%
2.1 × 10-511
 
1.6%
2.3 × 10-516
 
2.3%
4.1 × 10-523
 
3.4%
5 × 10-51
 
0.1%
5.1 × 10-530
 
4.4%
8.1 × 10-51
 
0.1%
0.0001 3
 
0.4%
0.000101 123
18.1%
0.000141 12
 
1.8%
ValueCountFrequency (%)
0.000641 2
 
0.3%
0.000601 5
 
0.7%
0.000401 8
1.2%
0.000321 1
 
0.1%
0.000301 13
1.9%
0.000201 16
2.3%
0.000161 8
1.2%
0.000151 10
1.5%
0.00015 1
 
0.1%
0.000141 12
1.8%
Distinct134
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size63.9 KiB
2024-03-21T10:19:54.051116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.9911894
Min length1

Characters and Unicode

Total characters6123
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)10.9%

Sample

1st row0.51/万分之 (4.6元)
2nd row0.51/万分之 (4.6元)
3rd row0.11/万分之 (1元)
4th row0.11/万分之 (1元)
5th row0.11/万分之 (1元)
ValueCountFrequency (%)
3.01 145
 
14.5%
1.01/万分之 123
 
12.3%
1.01 59
 
5.9%
0.11/万分之 34
 
3.4%
2 31
 
3.1%
0.51/万分之 30
 
3.0%
0.41/万分之 23
 
2.3%
6.01 22
 
2.2%
1.51 21
 
2.1%
2.5 20
 
2.0%
Other values (132) 491
49.1%
2024-03-21T10:19:54.860641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 977
16.0%
. 925
15.1%
0 606
9.9%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1389
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 977
16.0%
. 925
15.1%
0 606
9.9%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1389
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 977
16.0%
. 925
15.1%
0 606
9.9%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1389
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 977
16.0%
. 925
15.1%
0 606
9.9%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1389
22.7%

手续费标准-平昨-万分之
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)5.7%
Missing363
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean0.00010033019
Minimum1.1 × 10-5
Maximum0.000641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:55.038498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 10-5
5-th percentile1.1 × 10-5
Q14.1 × 10-5
median0.000101
Q30.000101
95-th percentile0.000304
Maximum0.000641
Range0.00063
Interquartile range (IQR)6 × 10-5

Descriptive statistics

Standard deviation0.00010282456
Coefficient of variation (CV)1.0248616
Kurtosis13.041736
Mean0.00010033019
Median Absolute Deviation (MAD)1 × 10-6
Skewness3.3362645
Sum0.031905
Variance1.0572891 × 10-8
MonotonicityNot monotonic
2024-03-21T10:19:55.219615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.000101 158
23.2%
1.1 × 10-534
 
5.0%
5.1 × 10-530
 
4.4%
4.1 × 10-523
 
3.4%
2.3 × 10-516
 
2.3%
2.1 × 10-511
 
1.6%
0.000151 10
 
1.5%
0.000401 8
 
1.2%
0.000161 8
 
1.2%
0.000201 5
 
0.7%
Other values (8) 15
 
2.2%
(Missing) 363
53.3%
ValueCountFrequency (%)
1.1 × 10-534
 
5.0%
2.1 × 10-511
 
1.6%
2.3 × 10-516
 
2.3%
4.1 × 10-523
 
3.4%
5 × 10-51
 
0.1%
5.1 × 10-530
 
4.4%
8.1 × 10-51
 
0.1%
0.0001 3
 
0.4%
0.000101 158
23.2%
0.00015 1
 
0.1%
ValueCountFrequency (%)
0.000641 2
 
0.3%
0.000601 5
 
0.7%
0.000401 8
 
1.2%
0.000321 1
 
0.1%
0.000301 1
 
0.1%
0.000201 5
 
0.7%
0.000161 8
 
1.2%
0.000151 10
 
1.5%
0.00015 1
 
0.1%
0.000101 158
23.2%
Distinct130
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size63.9 KiB
2024-03-21T10:19:56.168802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.989721
Min length1

Characters and Unicode

Total characters6122
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)10.1%

Sample

1st row0.51/万分之 (4.6元)
2nd row0.51/万分之 (4.6元)
3rd row0.11/万分之 (1元)
4th row0.11/万分之 (1元)
5th row0.11/万分之 (1元)
ValueCountFrequency (%)
1.01/万分之 158
 
15.8%
3.01 145
 
14.5%
1.01 59
 
5.9%
0.11/万分之 34
 
3.4%
2 31
 
3.1%
0.51/万分之 30
 
3.0%
0.41/万分之 23
 
2.3%
6.01 22
 
2.2%
1.51 21
 
2.1%
2.5 20
 
2.0%
Other values (125) 456
45.6%
2024-03-21T10:19:57.271580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1027
16.8%
. 925
15.1%
0 621
10.1%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1323
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1027
16.8%
. 925
15.1%
0 621
10.1%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1323
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1027
16.8%
. 925
15.1%
0 621
10.1%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1323
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1027
16.8%
. 925
15.1%
0 621
10.1%
318
 
5.2%
318
 
5.2%
) 318
 
5.2%
318
 
5.2%
( 318
 
5.2%
318
 
5.2%
/ 318
 
5.2%
Other values (9) 1323
21.6%

手续费标准-平今-万分之
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)5.0%
Missing363
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean0.00012549057
Minimum0
Maximum0.001201
Zeros72
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:57.443128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1 × 10-5
median0.000101
Q30.00014775
95-th percentile0.000304
Maximum0.001201
Range0.001201
Interquartile range (IQR)0.00013675

Descriptive statistics

Standard deviation0.0001697177
Coefficient of variation (CV)1.352434
Kurtosis23.765381
Mean0.00012549057
Median Absolute Deviation (MAD)6 × 10-5
Skewness4.279886
Sum0.039906
Variance2.8804099 × 10-8
MonotonicityNot monotonic
2024-03-21T10:19:57.608601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.000101 128
 
18.8%
0 72
 
10.6%
1.1 × 10-519
 
2.8%
0.00023 16
 
2.3%
0.000201 16
 
2.3%
0.000301 13
 
1.9%
0.000141 12
 
1.8%
0.000151 10
 
1.5%
0.000401 8
 
1.2%
0.000161 8
 
1.2%
Other values (6) 16
 
2.3%
(Missing) 363
53.3%
ValueCountFrequency (%)
0 72
10.6%
1.1 × 10-519
 
2.8%
5.1 × 10-54
 
0.6%
0.0001 3
 
0.4%
0.000101 128
18.8%
0.000141 12
 
1.8%
0.00015 1
 
0.1%
0.000151 10
 
1.5%
0.000161 8
 
1.2%
0.000201 16
 
2.3%
ValueCountFrequency (%)
0.001201 5
 
0.7%
0.000641 2
 
0.3%
0.000401 8
1.2%
0.000321 1
 
0.1%
0.000301 13
1.9%
0.00023 16
2.3%
0.000201 16
2.3%
0.000161 8
1.2%
0.000151 10
1.5%
0.00015 1
 
0.1%
Distinct130
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
2024-03-21T10:19:58.595662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.9720999
Min length1

Characters and Unicode

Total characters5429
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)12.0%

Sample

1st row0.51/万分之 (4.6元)
2nd row0.51/万分之 (4.6元)
3rd row0.11/万分之 (1元)
4th row0.11/万分之 (1元)
5th row0.11/万分之 (1元)
ValueCountFrequency (%)
0 130
 
13.0%
1.01/万分之 128
 
12.8%
0元 72
 
7.2%
0/万分之 72
 
7.2%
3.01 66
 
6.6%
1.01 59
 
5.9%
6.01 24
 
2.4%
1.51 21
 
2.1%
2.5 20
 
2.0%
0.11/万分之 19
 
1.9%
Other values (124) 388
38.8%
2024-03-21T10:19:59.748815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 765
14.1%
. 671
12.4%
0 666
12.3%
318
 
5.9%
318
 
5.9%
318
 
5.9%
318
 
5.9%
( 318
 
5.9%
318
 
5.9%
) 318
 
5.9%
Other values (9) 1101
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 765
14.1%
. 671
12.4%
0 666
12.3%
318
 
5.9%
318
 
5.9%
318
 
5.9%
318
 
5.9%
( 318
 
5.9%
318
 
5.9%
) 318
 
5.9%
Other values (9) 1101
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 765
14.1%
. 671
12.4%
0 666
12.3%
318
 
5.9%
318
 
5.9%
318
 
5.9%
318
 
5.9%
( 318
 
5.9%
318
 
5.9%
) 318
 
5.9%
Other values (9) 1101
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 765
14.1%
. 671
12.4%
0 666
12.3%
318
 
5.9%
318
 
5.9%
318
 
5.9%
318
 
5.9%
( 318
 
5.9%
318
 
5.9%
) 318
 
5.9%
Other values (9) 1101
20.3%

每跳毛利
Real number (ℝ)

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.04699
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:19:59.910153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q325
95-th percentile60
Maximum100
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.741848
Coefficient of variation (CV)0.89998079
Kurtosis4.2665769
Mean23.04699
Median Absolute Deviation (MAD)5
Skewness2.0215496
Sum15695
Variance430.22426
MonotonicityNot monotonic
2024-03-21T10:20:00.056048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 270
39.6%
20 99
 
14.5%
25 89
 
13.1%
50 82
 
12.0%
5 62
 
9.1%
100 22
 
3.2%
30 20
 
2.9%
15 12
 
1.8%
40 11
 
1.6%
60 8
 
1.2%
ValueCountFrequency (%)
5 62
 
9.1%
10 270
39.6%
15 12
 
1.8%
20 99
 
14.5%
25 89
 
13.1%
30 20
 
2.9%
40 11
 
1.6%
50 82
 
12.0%
60 8
 
1.2%
80 6
 
0.9%
ValueCountFrequency (%)
100 22
 
3.2%
80 6
 
0.9%
60 8
 
1.2%
50 82
 
12.0%
40 11
 
1.6%
30 20
 
2.9%
25 89
 
13.1%
20 99
 
14.5%
15 12
 
1.8%
10 270
39.6%

手续费
Real number (ℝ)

Distinct151
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.135095
Minimum0.3
Maximum297.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-03-21T10:20:00.220945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile2
Q13
median6
Q312
95-th percentile69.9
Maximum297.9
Range297.6
Interquartile range (IQR)9

Descriptive statistics

Standard deviation42.151591
Coefficient of variation (CV)2.3243104
Kurtosis24.603441
Mean18.135095
Median Absolute Deviation (MAD)3.4
Skewness4.7271849
Sum12350
Variance1776.7567
MonotonicityNot monotonic
2024-03-21T10:20:00.456826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 96
 
14.1%
2 85
 
12.5%
6 66
 
9.7%
4 25
 
3.7%
12 24
 
3.5%
20 24
 
3.5%
5 22
 
3.2%
2.4 14
 
2.1%
8 12
 
1.8%
2.9 11
 
1.6%
Other values (141) 302
44.3%
ValueCountFrequency (%)
0.3 8
 
1.2%
0.9 11
 
1.6%
1.5 3
 
0.4%
1.7 11
 
1.6%
2 85
12.5%
2.3 4
 
0.6%
2.4 14
 
2.1%
2.5 1
 
0.1%
2.6 4
 
0.6%
2.7 4
 
0.6%
ValueCountFrequency (%)
297.9 1
0.1%
296.8 1
0.1%
295.8 1
0.1%
291.4 1
0.1%
275.1 1
0.1%
274.4 1
0.1%
274 1
0.1%
271 1
0.1%
262.3 1
0.1%
262.1 1
0.1%

每跳净利
Real number (ℝ)

Distinct166
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9118943
Minimum-257.9
Maximum97
Zeros0
Zeros (%)0.0%
Negative106
Negative (%)15.6%
Memory size5.4 KiB
2024-03-21T10:20:00.659480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-257.9
5-th percentile-34.7
Q12.7
median7
Q315
95-th percentile47
Maximum97
Range354.9
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation40.024196
Coefficient of variation (CV)8.1484238
Kurtosis21.143645
Mean4.9118943
Median Absolute Deviation (MAD)5.1
Skewness-3.8102444
Sum3345
Variance1601.9363
MonotonicityNot monotonic
2024-03-21T10:20:00.847273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 66
 
9.7%
8 47
 
6.9%
4 42
 
6.2%
3 36
 
5.3%
15 21
 
3.1%
80 19
 
2.8%
47 16
 
2.3%
6 14
 
2.1%
19 13
 
1.9%
-1 13
 
1.9%
Other values (156) 394
57.9%
ValueCountFrequency (%)
-257.9 1
0.1%
-256.8 1
0.1%
-255.8 1
0.1%
-251.4 1
0.1%
-235.1 1
0.1%
-234.4 1
0.1%
-234 1
0.1%
-231 1
0.1%
-202.3 1
0.1%
-202.1 1
0.1%
ValueCountFrequency (%)
97 3
 
0.4%
80 19
2.8%
47.7 4
 
0.6%
47.6 7
 
1.0%
47 16
2.3%
46.6 7
 
1.0%
41.2 1
 
0.1%
37 3
 
0.4%
32.8 1
 
0.1%
32.6 1
 
0.1%

备注
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.4%
Missing609
Missing (%)89.4%
Memory size40.7 KiB
主力合约
72 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters288
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row主力合约
2nd row主力合约
3rd row主力合约
4th row主力合约
5th row主力合约

Common Values

ValueCountFrequency (%)
主力合约 72
 
10.6%
(Missing) 609
89.4%

Length

2024-03-21T10:20:01.051890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T10:20:01.206894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
主力合约 72
100.0%

Most occurring characters

ValueCountFrequency (%)
72
25.0%
72
25.0%
72
25.0%
72
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72
25.0%
72
25.0%
72
25.0%
72
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72
25.0%
72
25.0%
72
25.0%
72
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72
25.0%
72
25.0%
72
25.0%
72
25.0%

手续费更新时间
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2023-12-29 02:49:54.559000
Maximum2023-12-29 02:49:54.559000
2024-03-21T10:20:01.330638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:01.497962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

价格更新时间
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2024-01-02 15:25:32.979000
Maximum2024-01-02 15:25:32.979000
2024-03-21T10:20:01.643052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:01.791907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-21T10:19:42.240129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:20.577587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.470277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.228608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.191282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.231447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:30.143475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.213521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.168042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.108457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:38.054823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.139696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:42.390670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:20.726369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.600850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.382689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.327758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.367121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:30.317979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.361178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.331672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.285815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:38.196436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.286196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:42.542053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:20.859094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.725538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.537722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.477341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.498554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:30.497762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.509543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.482593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.431213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:38.360107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.429259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:42.738700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.025708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.896999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.739525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.645798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.664531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:30.701281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.660236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.628567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.562022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:38.761169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.626351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:42.889909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.171964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.024021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.878192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.787993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.821253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:30.861809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.857903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.780888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.724916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:38.892650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.777398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.042547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.336190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.159534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.044075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.933362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.963023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.023203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.017130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.935297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:36.924967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.022952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:40.944460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.221070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.507781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.312456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.231595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:27.104129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.147687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.215108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.171873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.119805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.097716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.191752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:41.113026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.394117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.696626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.465735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.393315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:27.270401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.312479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.391016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.338676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.285691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.260332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.355805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:41.297735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.542604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:21.844900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.625053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.531953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:27.422827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.491219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.546488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.500400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.431379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.421206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.508673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:41.476022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.710195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.001468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.769780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.686245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:27.808765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.639999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.704815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.679873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.586516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.572913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.669123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:41.658768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:43.870393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.144067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:23.938307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:25.859476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:27.931086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.814071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:31.855905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:33.844573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.764867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.751004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.814931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:41.826345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:44.055355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:22.301259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:24.079620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:26.024651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:28.074375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:29.977093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:32.021616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:34.005659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:35.924401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:37.915406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:39.989016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:42.062958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-21T10:19:44.330899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:19:44.888146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

交易所名称合约名称合约代码现价涨停板跌停板保证金-买开保证金-卖开保证金-每手手续费标准-开仓-万分之手续费标准-开仓-元手续费标准-平昨-万分之手续费标准-平昨-元手续费标准-平今-万分之手续费标准-平今-元每跳毛利手续费每跳净利备注手续费更新时间价格更新时间
0上海期货交易所沪银2401ag24015996.06415.05576.015.015.013491.000.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)159.25.8None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
1上海期货交易所沪银2402ag24026012.06432.05591.010.010.09018.000.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)159.25.8主力合约2023-12-29 02:49:54.5592024-01-02 15:25:32.979
2上海期货交易所沪银2403ag24036012.06432.05591.09.09.08116.200.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
3上海期货交易所沪银2404ag24046021.06442.05599.09.09.08128.350.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
4上海期货交易所沪银2405ag24056031.06453.05608.09.09.08141.850.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
5上海期货交易所沪银2406ag24066028.06449.05606.09.09.08137.800.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)0.0000510.51/万分之 (4.6元)159.25.8None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
6上海期货交易所沪银2407ag24076041.06463.05618.09.09.08155.350.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
7上海期货交易所沪银2408ag24086052.06475.05628.09.09.08170.200.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
8上海期货交易所沪银2409ag24096053.06476.05629.09.09.08171.550.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
9上海期货交易所沪银2410ag24106057.06480.05633.09.09.08176.950.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)0.0000110.11/万分之 (1元)152.013.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
交易所名称合约名称合约代码现价涨停板跌停板保证金-买开保证金-卖开保证金-每手手续费标准-开仓-万分之手续费标准-开仓-元手续费标准-平昨-万分之手续费标准-平昨-元手续费标准-平今-万分之手续费标准-平今-元每跳毛利手续费每跳净利备注手续费更新时间价格更新时间
671中国金融期货交易所10年国债2409T2409102.6150104.6650100.5652.02.020523.00NaN3.01NaN3.01NaN0503.047.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
672中国金融期货交易所5年期国债2403TF2403102.5450103.7750101.3151.21.212305.40NaN3.01NaN3.01NaN0503.047.0主力合约2023-12-29 02:49:54.5592024-01-02 15:25:32.979
673中国金融期货交易所5年期国债2406TF2406102.4500103.6750101.2251.21.212294.00NaN3.01NaN3.01NaN0503.047.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
674中国金融期货交易所5年期国债2409TF2409102.3700103.5950101.1451.21.212284.40NaN3.01NaN3.01NaN0503.047.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
675中国金融期货交易所30年期国债期货2403TL2403101.7000105.250098.1503.53.535595.00NaN3.01NaN3.01NaN01003.097.0主力合约2023-12-29 02:49:54.5592024-01-02 15:25:32.979
676中国金融期货交易所30年期国债期货2406TL2406101.5000105.050097.9503.53.535525.00NaN3.01NaN3.01NaN01003.097.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
677中国金融期货交易所30年期国债期货2409TL2409101.4200104.960097.8803.53.535497.00NaN3.01NaN3.01NaN01003.097.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
678中国金融期货交易所2年期国债2403TS2403101.3220101.8280100.8160.50.510132.20NaN3.01NaN3.01NaN0403.037.0主力合约2023-12-29 02:49:54.5592024-01-02 15:25:32.979
679中国金融期货交易所2年期国债2406TS2406101.3280101.8340100.8220.50.510132.80NaN3.01NaN3.01NaN0403.037.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979
680中国金融期货交易所2年期国债2409TS2409101.3139101.8199100.8080.50.510131.39NaN3.01NaN3.01NaN0403.037.0None2023-12-29 02:49:54.5592024-01-02 15:25:32.979